def get_mean_envelope(x):
y = np.dot(x, spatial)
envelope = np.abs(hilbert(fft_filter(y, fs, mu_band)))
n_samples = len(envelope) // 10
return [
envelope[k * n_samples:(k + 1) * n_samples].mean()
for k in range(10)
]
def preproc(x, fs, rej=None):
x = dc_blocker(x)
x = fft_filter(x, fs, band=(0, 45))
if rej is not None:
x = np.dot(x, rej)
return x
elif j > len(p_names) - 3:
x = preproc(f['protocol{}/raw_data'.format(j + 1)][:], fs, rejection if reject else None)
print(x.shape)
raw_after = add_data_simple(raw_after, name, x)
# plot raw data
ch_plot = ['C3', 'C4', 'P3', 'P4']#, 'Pz', 'Fp1']
fig1, axes = plt.subplots(len(ch_plot), ncols=1, sharex=True, sharey=True)
print(axes)
#find median
x_all = []
for name, x in list(raw_before.items()) + list(raw_after.items()):
x_all.append(np.abs(hilbert(fft_filter(x, fs, (4, 8)))))
x_median = np.mean(np.concatenate(x_all), 0)
print(x_median)
# plot raw
t = 0
cm = get_colors()
for name, x in list(raw_before.items()) + list(raw_after.items()):
if name in ['Closed', 'Opened', 'Baseline', 'Left', 'Right', 'FB']:
for j, ch in enumerate(ch_plot):
time = np.arange(t, t + len(x)) / fs
x_plot = fft_filter(x[:, channels.index(ch)], fs, band=(3, 45)) if ch != 'Fp1' else x[:, channels.index(ch)]
axes[j].plot(time, x_plot, c=cm[name], alpha=1)
x_plot = fft_filter(x[:, channels.index(ch)], fs, band=(9, 14)) if ch != 'Fp1' else x[:, channels.index(ch)]
x_plot = np.abs(hilbert(x_plot))
rejection, alpha, ica = load_rejections(f, reject_alpha=True)
raw_before = OrderedDict()
for j, name in enumerate(p_names):
x = preproc(f['protocol{}/raw_data'.format(j + 1)][:], fs,
rejection if reject else None)
raw_before = add_data(raw_before, name, x, j)
# plot raw data
ch_plot = ['C3', 'C4', 'P3', 'P4'] #, 'Pz', 'Fp1']
fig1, axes = plt.subplots(len(ch_plot), ncols=1, sharex=True, sharey=True)
print(axes)
#find median
x_all = []
for name, x in raw_before.items():
x_all.append(np.abs(hilbert(fft_filter(x, fs, (4, 8)))))
x_median = np.mean(np.concatenate(x_all), 0)
print(x_median)
# plot raw
t = 0
cm = get_colors_f
for name, x in list(raw_before.items()):
for j, ch in enumerate(ch_plot):
time = np.arange(t, t + len(x)) / fs
x_plot = fft_filter(x[:, channels.index(ch)], fs, band=(
3, 45)) if ch != 'Fp1' else x[:, channels.index(ch)]
axes[j].plot(time, x_plot, c=cm(name), alpha=1)
x_plot = fft_filter(x[:, channels.index(ch)], fs, band=(
9, 14)) if ch != 'Fp1' else x[:, channels.index(ch)]
x_plot = np.abs(hilbert(x_plot))
#plt.show()
print('ww', sum(get_outliers_mask(data[channels][data['block_number']<3])))
# spatial filter (SMR detection)
try:
filt = np.load('ica_{}.npy'.format(experiment))
#filt = np.zeros(len(channels))
#filt[channels.index('C3')] = 1
except FileNotFoundError:
a = QApplication([])
(rej, filt, topo, _unmix, _bandpass, _) = ICADialog.get_rejection(
data[channels][data['block_number'].isin([1, 2])]#.iloc[~get_outliers_mask(data[channels][data['block_number']<12], std=2)]
, channels, fs, mode='csp')
#(_rej, filt, topo, _unmix, _bandpass, _) = ICADialog.get_rejection(np.concatenate(list(x[y=='Left']) + l1ist(x[y=='Legs'])), channels, fs, mode='csp')
# filt, topography, bandpass, rejections = SelectSSDFilterWidget.select_filter_and_bandpass(np.concatenate(x), ch_names_to_2d_pos(channels), channels, sampling_freq=fs)
np.save('ica_{}.npy'.format(experiment), filt)
data['SMR'] = np.dot(data[channels], filt)
#data = data.iloc[~get_outliers_mask(data[['C3', 'SMR']], std=3, iter_numb=10)]
# temporal filter
data['SMR_band_filt'] = fft_filter(data['SMR'], fs, (11, 13))
data['SMR_env'] = np.abs(signal.hilbert(data['SMR_band_filt']))
data.to_pickle('data-{}.pkl'.format(experiment))
data['SMR'].plot()
data['SMR_band_filt'].plot()
data['SMR_env'].plot()
plt.show()
raw = OrderedDict()
signal = OrderedDict()
for j, name in enumerate(p_names):
if name != 'Bci':
x = preproc(f['protocol{}/raw_data'.format(j + 1)][:], fs)
raw = add_data(raw, name, x, j)
signal = add_data(signal, name,
f['protocol{}/signals_data'.format(j + 1)][:], j)
ch_plot = ['C3', 'P3', 'ICA'] # , 'Pz', 'Fp1']
fig1, axes = plt.subplots(len(ch_plot), ncols=1, sharex=True, sharey=False)
# find median
x_all = []
x_all_ica = []
for name, x in raw.items():
if 'Baseline' in name:
x_all.append(np.abs(hilbert(fft_filter(x, fs, band=mu_band))))
x_all_ica.append(
np.abs(
hilbert(np.dot(fft_filter(x, fs, band=mu_band), spatial))))
break
x_median = np.median(np.concatenate(x_all), 0)
x_f_median = np.median(np.concatenate(x_all_ica))
coef = 1
# plot raw
t = 0
cm = get_colors_f
fff = plt.figure()
for name, x in list(raw.items()):
for j, ch in enumerate(ch_plot):
print(day, subj, experiment)
if subj > 4:
with h5py.File('{}\\{}\\{}'.format(dir_, experiment,
'experiment_data.h5')) as f:
fs, channels, p_names = get_info(f, settings['drop_channels'])
spatial = f['protocol15/signals_stats/left/spatial_filter'][:]
band = f['protocol15/signals_stats/left/bandpass'][:]
print('band:', band)
signals = OrderedDict()
raw = OrderedDict()
for j, name in enumerate(p_names):
if name != 'Bci':
x = np.dot(f['protocol{}/raw_data'.format(j + 1)][:],
spatial)
x = drop_outliers(x)
x = fft_filter(x, fs, band=band)
#x[x > 0.00005] = np.nan
signals = add_data(signals, name, x, j)
raw = add_data(
raw, name,
f['protocol{}/raw_data'.format(j + 1)][:], j)
norm_coeff = np.std(signals['16. Baseline'])
fb_vars = []
for fb_name in ['18. FB', '20. FB', '22. FB']:
for fb_part in np.split(
signals[fb_name],
range(0, len(signals[fb_name]), 2 * fs))[1:-1]:
fb_vars.append(fb_part.std())
fb_vars = np.array(fb_vars) / norm_coeff
#sns.regplot(np.arange(len(fb_vars)), fb_vars)
print(mock_ind)
df = pd.DataFrame(np.concatenate(data), columns=channels)
df['block_name'] = np.concatenate([[p]*len(d) for p, d in zip(p_names, data)])
df['block_number'] = np.concatenate([[j + 1]*len(d) for j, d in enumerate(data)])
df['alpha'] = np.dot(df[channels], spat)
df['alpha_env'] = df['alpha']*0
df['time'] = np.arange(len(df)) / fs
df['signal'] = np.concatenate(signal)
fig, axes = plt.subplots(1, 5)
eeg = fft_filter(df.loc[df['block_name'].isin(['Open', 'Close']), channels], fs, band)
topo = np.dot(np.dot(eeg.T, eeg), spat)
axes[2].set_xlabel('Spat. filt.')
plot_topomap(spat, montage.get_pos(), axes=axes[2], show=False)
axes[3].set_xlabel('Topography')
plot_topomap(topo, montage.get_pos(), axes=axes[3], show=False)
axes[0].plot(df.loc[df['block_number']==2, 'time'], fft_filter(df.loc[df['block_number']==2, 'alpha'], fs, (2, 100)))
axes[0].set_xlim(31, 32)
axes[0].set_xlabel('Time, s')
axes[0].set_ylabel('Voltage, $\mu V$')
#axes[2].plot(fft_filter(df.loc[df['block_number']==2, 'alpha'], fs, (1, 45)))
axes[1].plot(*welch(df.loc[df['block_name'].isin(['Close']), 'alpha'], fs, nperseg=fs*4))
axes[1].plot(*welch(df.loc[df['block_name'].isin(['Open']), 'alpha'], fs, nperseg=fs*4))
print(mock_ind)
df = pd.DataFrame(np.concatenate(data), columns=channels)
df['block_name'] = np.concatenate([[p]*len(d) for p, d in zip(p_names, data)])
df['block_number'] = np.concatenate([[j + 1]*len(d) for j, d in enumerate(data)])
df['alpha'] = np.dot(df[channels], spat)
df['alpha_env'] = df['alpha']*0
df['time'] = np.arange(len(df)) / fs
df['times'] = np.concatenate(times)
df['signal'] = np.concatenate(signal)[:, 0]
fig, axes = plt.subplots(1, 5)
eeg = fft_filter(df.loc[df['block_name'].isin(['Legs', 'Left']), channels], fs, band)
topo = np.dot(np.dot(eeg.T, eeg), spat)
axes[2].set_xlabel('Spat. filt.')
plot_topomap(spat, montage.get_pos(), axes=axes[2], show=False, contours=0)
axes[3].set_xlabel('Topography')
plot_topomap(topo, montage.get_pos(), axes=axes[3], show=False, contours=0)
axes[0].plot(df.loc[df['block_number']==1, 'time'], 1000000*fft_filter(df.loc[df['block_number']==1, 'alpha'], fs, (2, 100)))
axes[0].set_xlim(3, 4)
axes[0].set_xlabel('Time, s')
axes[0].set_ylabel('Voltage, $\mu V$')
#axes[2].plot(fft_filter(df.loc[df['block_number']==2, 'alpha'], fs, (1, 45)))
axes[1].plot(*welch(df.loc[df['block_name'].isin(['Left']), 'alpha']*1000000, fs, nperseg=fs*4))
axes[1].plot(*welch(df.loc[df['block_name'].isin(['Legs']), 'alpha']*1000000, fs, nperseg=fs*4))
df = pd.DataFrame(np.concatenate(data), columns=channels)
df['block_name'] = np.concatenate([[p]*len(d) for p, d in zip(p_names, data)])
df['block_number'] = np.concatenate([[j + 1]*len(d) for j, d in enumerate(data)])
df['times'] = np.concatenate([np.arange(len(d)) for d in data])
df = df[df['block_name'].isin(['Legs', 'Rest', 'Left', 'Right'])]
#
eeg = df[channels].as_matrix()
try:
filters = np.load('filters_ex.npy')
topos = np.load('topos_ex.npy')
except FileNotFoundError:
ica = ICADecomposition(channels, fs, (0.5, 45))
scores, filters, topos = ica.decompose(eeg)
np.save('filters_ex.npy', filters)
np.save('topos_ex.npy', topos)
sources = np.dot(eeg, filters)
for state in ['Legs', 'Left', 'Right']:
smr_ind = np.argmax(sources[df.block_name == 'Rest'].std(0)/sources[df.block_name == state].std(0))
df[state+'SMR'] = np.dot(df[channels], filters[:, smr_ind])
df[state + 'SMR-env'] = np.abs(hilbert(fft_filter(df[state+'SMR'], fs, [9, 13])))
#plt.plot(np.dot(df[channels], filters[:, smr_ind]))
#plt.show()
#plot_topomap(topos[:, smr_ind], montage.get_pos())
df.to_csv('wow_ex.csv')
sns.tsplot(df, time='times', unit='block_number', value='LegsSMR-env', condition='block_name')
plt.show()
def plot_results(pilot_dir,
subj,
channel,
alpha_band=(9, 14),
theta_band=(3, 6),
drop_channels=None,
dc=False,
reject_alpha=True,
normalize_by='opened'):
drop_channels = drop_channels or []
cm = get_colors()
fg = plt.figure(figsize=(30, 6))
for j_s, experiment in enumerate(subj):
with h5py.File('{}\\{}\\{}'.format(pilot_dir, experiment,
'experiment_data.h5')) as f:
rejections, top_alpha, top_ica = load_rejections(
f, reject_alpha=reject_alpha)
fs, channels, p_names = get_info(f, drop_channels)
ch = channels.index(channel)
#plt.plot(fft_filter(f['protocol6/raw_data'][:, ch], fs, band=(3, 35)))
#plt.plot(fft_filter(np.dot(f['protocol6/raw_data'], rejections)[:, ch], fs, band=(3, 35)))
#plt.show()
#from scipy.signal import welch
#plt.plot(*welch(f['protocol1/raw_data'][:60*500//2, channels.index('C3')], fs, nperseg=1000))
#plt.plot(*welch(f['protocol1/raw_data'][60*500//2:, channels.index('C3')], fs, nperseg=1000))
#plt.plot(*welch(f['protocol2/raw_data'][:30*500//2, channels.index('C3')], fs, nperseg=1000))
#plt.plot(*welch(f['protocol2/raw_data'][30*500//2:, channels.index('C3')], fs, nperseg=1000))
#plt.legend(['Close', 'Open', 'Left', 'Right'])
#plt.show()
# collect powers
powers = OrderedDict()
raw = OrderedDict()
alpha = OrderedDict()
pow_theta = []
for j, name in enumerate(p_names):
pow, alpha_x, x = get_protocol_power(f,
j,
fs,
rejections,
ch,
alpha_band,
dc=dc)
if 'FB' in name:
pow_theta.append(
get_protocol_power(f,
j,
fs,
rejections,
ch,
theta_band,
dc=dc)[0].mean())
powers = add_data(powers, name, pow, j)
raw = add_data(raw, name, x, j)
alpha = add_data(alpha, name, alpha_x, j)
# plot rejections
n_tops = top_ica.shape[1] + top_alpha.shape[1]
for j_t in range(top_ica.shape[1]):
ax = fg.add_subplot(
4, n_tops * len(subj),
n_tops * len(subj) * 3 + n_tops * j_s + j_t + 1)
ax.set_xlabel('ICA{}'.format(j_t + 1))
labels, fs = get_lsl_info_from_xml(f['stream_info.xml'][0])
channels = [
label for label in labels if label not in drop_channels
]
pos = ch_names_to_2d_pos(channels)
plot_topomap(data=top_ica[:, j_t],
pos=pos,
axes=ax,
show=False)
for j_t in range(top_alpha.shape[1]):
ax = fg.add_subplot(
4, n_tops * len(subj),
n_tops * len(subj) * 3 + n_tops * j_s + j_t + 1 +
top_ica.shape[1])
ax.set_xlabel('CSP{}'.format(j_t + 1))
labels, fs = get_lsl_info_from_xml(f['stream_info.xml'][0])
channels = [
label for label in labels if label not in drop_channels
]
pos = ch_names_to_2d_pos(channels)
plot_topomap(data=top_alpha[:, j_t],
pos=pos,
axes=ax,
show=False)
# plot powers
if normalize_by == 'opened':
norm = powers['1. Opened'].mean()
elif normalize_by == 'beta':
norm = np.mean(pow_theta)
else:
print('WARNING: norm = 1')
print('norm', norm)
ax1 = fg.add_subplot(3, len(subj), j_s + 1)
ax = fg.add_subplot(3, len(subj), j_s + len(subj) + 1)
t = 0
for j_p, ((name, pow),
(name, x)) in enumerate(zip(powers.items(),
raw.items())):
if name == '2228. FB':
from scipy.signal import periodogram
fff = plt.figure()
fff.gca().plot(*periodogram(x, fs, nfft=fs * 3),
c=cm[name.split()[1]])
plt.xlim(0, 80)
plt.ylim(0, 3e-11)
plt.show()
print(name)
time = np.arange(t, t + len(x)) / fs
color = cm[''.join(
[i for i in name.split()[1] if not i.isdigit()])]
ax1.plot(time, fft_filter(x, fs, (2, 45)), c=color, alpha=0.4)
ax1.plot(time, alpha[name], c=color)
t += len(x)
ax.plot([j_p], [pow.mean() / norm],
'o',
c=color,
markersize=10)
ax.errorbar([j_p], [pow.mean() / norm],
yerr=pow.std() / norm,
c=color,
ecolor=color)
fb_x = np.hstack([[j] * len(pows)
for j, (key, pows) in enumerate(powers.items())
if 'FB' in key])
fb_y = np.hstack(
[pows for key, pows in powers.items() if 'FB' in key]) / norm
sns.regplot(x=fb_x,
y=fb_y,
ax=ax,
color=cm['FB'],
scatter=False,
truncate=True)
ax1.set_xlim(0, t / fs)
ax1.set_ylim(-40, 40)
plt.setp(ax.xaxis.get_majorticklabels(), rotation=70)
ax.set_xticks(range(len(powers)))
ax.set_xticklabels(powers.keys())
ax.set_ylim(0, 3)
ax.set_xlim(-1, len(powers))
ax1.set_title('Day {}'.format(j_s + 1))
return fg
def get_mean_envelope(x):
y = np.dot(x, spatial)
envelope = np.abs(hilbert(fft_filter(y, fs, mu_band)))
n_samples = len(envelope) // 10
return [envelope[k * n_samples: (k+1) * n_samples].mean() for k in range(10)]
axes[j, 1].set_xlabel('Spatial filter ({})'.format('before' if j == 0 else 'after'))
#plt.show()
# plot raw data
ch_plot = ['C3', 'P3', 'ICA']#, 'Pz', 'Fp1']
fig1, axes = plt.subplots(len(ch_plot), ncols=1, sharex=True, sharey=False)
print(axes)
#find median
x_all = []
x_all_ica = []
for name, x in raw.items():
if 'Baseline' in name:
x_all.append(np.abs(hilbert(fft_filter(x, fs, band=mu_band))))
x_all_ica.append(np.abs(hilbert(np.dot(fft_filter(x, fs, band=mu_band), spatial))))
break
x_median = np.median(np.concatenate(x_all), 0)
x_f_median = np.median(np.concatenate(x_all_ica))
coef = 1
# plot raw
t = 0
cm = get_colors_f
fff = plt.figure()
for name, x in list(raw.items()):
for j, ch in enumerate(ch_plot):
time = np.arange(t, t + len(x)) / fs
y = x[:, channels.index(ch)] if ch != 'ICA' else np.dot(x, spatial)
#
eeg = df[channels].as_matrix()
try:
filters = np.load('filters_ex.npy')
topos = np.load('topos_ex.npy')
except FileNotFoundError:
ica = ICADecomposition(channels, fs, (0.5, 45))
scores, filters, topos = ica.decompose(eeg)
np.save('filters_ex.npy', filters)
np.save('topos_ex.npy', topos)
sources = np.dot(eeg, filters)
for state in ['Legs', 'Left', 'Right']:
smr_ind = np.argmax(sources[df.block_name == 'Rest'].std(0) /
sources[df.block_name == state].std(0))
df[state + 'SMR'] = np.dot(df[channels], filters[:, smr_ind])
df[state + 'SMR-env'] = np.abs(
hilbert(fft_filter(df[state + 'SMR'], fs, [9, 13])))
#plt.plot(np.dot(df[channels], filters[:, smr_ind]))
#plt.show()
#plot_topomap(topos[:, smr_ind], montage.get_pos())
df.to_csv('wow_ex.csv')
sns.tsplot(df,
time='times',
unit='block_number',
value='LegsSMR-env',
condition='block_name')
plt.show()
def plot_results(pilot_dir, subj, channel, alpha_band=(9, 14), theta_band=(3, 6), drop_channels=None, dc=False,
reject_alpha=True, normalize_by='opened'):
drop_channels = drop_channels or []
cm = get_colors()
fg = plt.figure(figsize=(30, 6))
for j_s, experiment in enumerate(subj):
with h5py.File('{}\\{}\\{}'.format(pilot_dir, experiment, 'experiment_data.h5')) as f:
rejections, top_alpha, top_ica = load_rejections(f, reject_alpha=reject_alpha)
fs, channels, p_names = get_info(f, drop_channels)
ch = channels.index(channel)
#plt.plot(fft_filter(f['protocol6/raw_data'][:, ch], fs, band=(3, 35)))
#plt.plot(fft_filter(np.dot(f['protocol6/raw_data'], rejections)[:, ch], fs, band=(3, 35)))
#plt.show()
#from scipy.signal import welch
#plt.plot(*welch(f['protocol1/raw_data'][:60*500//2, channels.index('C3')], fs, nperseg=1000))
#plt.plot(*welch(f['protocol1/raw_data'][60*500//2:, channels.index('C3')], fs, nperseg=1000))
#plt.plot(*welch(f['protocol2/raw_data'][:30*500//2, channels.index('C3')], fs, nperseg=1000))
#plt.plot(*welch(f['protocol2/raw_data'][30*500//2:, channels.index('C3')], fs, nperseg=1000))
#plt.legend(['Close', 'Open', 'Left', 'Right'])
#plt.show()
# collect powers
powers = OrderedDict()
raw = OrderedDict()
alpha = OrderedDict()
pow_theta = []
for j, name in enumerate(p_names):
pow, alpha_x, x = get_protocol_power(f, j, fs, rejections, ch, alpha_band, dc=dc)
if 'FB' in name:
pow_theta.append(get_protocol_power(f, j, fs, rejections, ch, theta_band, dc=dc)[0].mean())
powers = add_data(powers, name, pow, j)
raw = add_data(raw, name, x, j)
alpha = add_data(alpha, name, alpha_x, j)
# plot rejections
n_tops = top_ica.shape[1] + top_alpha.shape[1]
for j_t in range(top_ica.shape[1]):
ax = fg.add_subplot(4, n_tops * len(subj), n_tops * len(subj) * 3 + n_tops * j_s + j_t + 1)
ax.set_xlabel('ICA{}'.format(j_t + 1))
labels, fs = get_lsl_info_from_xml(f['stream_info.xml'][0])
channels = [label for label in labels if label not in drop_channels]
pos = ch_names_to_2d_pos(channels)
plot_topomap(data=top_ica[:, j_t], pos=pos, axes=ax, show=False)
for j_t in range(top_alpha.shape[1]):
ax = fg.add_subplot(4, n_tops * len(subj),
n_tops * len(subj) * 3 + n_tops * j_s + j_t + 1 + top_ica.shape[1])
ax.set_xlabel('CSP{}'.format(j_t + 1))
labels, fs = get_lsl_info_from_xml(f['stream_info.xml'][0])
channels = [label for label in labels if label not in drop_channels]
pos = ch_names_to_2d_pos(channels)
plot_topomap(data=top_alpha[:, j_t], pos=pos, axes=ax, show=False)
# plot powers
if normalize_by == 'opened':
norm = powers['1. Opened'].mean()
elif normalize_by == 'beta':
norm = np.mean(pow_theta)
else:
print('WARNING: norm = 1')
print('norm', norm)
ax1 = fg.add_subplot(3, len(subj), j_s + 1)
ax = fg.add_subplot(3, len(subj), j_s + len(subj) + 1)
t = 0
for j_p, ((name, pow), (name, x)) in enumerate(zip(powers.items(), raw.items())):
if name == '2228. FB':
from scipy.signal import periodogram
fff = plt.figure()
fff.gca().plot(*periodogram(x, fs, nfft=fs * 3), c=cm[name.split()[1]])
plt.xlim(0, 80)
plt.ylim(0, 3e-11)
plt.show()
print(name)
time = np.arange(t, t + len(x)) / fs
color = cm[''.join([i for i in name.split()[1] if not i.isdigit()])]
ax1.plot(time, fft_filter(x, fs, (2, 45)), c=color, alpha=0.4)
ax1.plot(time, alpha[name], c=color)
t += len(x)
ax.plot([j_p], [pow.mean() / norm], 'o', c=color, markersize=10)
ax.errorbar([j_p], [pow.mean() / norm], yerr=pow.std() / norm, c=color, ecolor=color)
fb_x = np.hstack([[j] * len(pows) for j, (key, pows) in enumerate(powers.items()) if 'FB' in key])
fb_y = np.hstack([pows for key, pows in powers.items() if 'FB' in key]) / norm
sns.regplot(x=fb_x, y=fb_y, ax=ax, color=cm['FB'], scatter=False, truncate=True)
ax1.set_xlim(0, t / fs)
ax1.set_ylim(-40, 40)
plt.setp(ax.xaxis.get_majorticklabels(), rotation=70)
ax.set_xticks(range(len(powers)))
ax.set_xticklabels(powers.keys())
ax.set_ylim(0, 3)
ax.set_xlim(-1, len(powers))
ax1.set_title('Day {}'.format(j_s + 1))
return fg
#plt.show()
print('ww', sum(get_outliers_mask(data[channels][data['block_number']<3])))
# spatial filter (SMR detection)
try:
filt = np.load('ica_{}.npy'.format(experiment))
#filt = np.zeros(len(channels))
#filt[channels.index('C3')] = 1
except FileNotFoundError:
a = QApplication([])
(rej, filt, topo, _unmix, _bandpass, _) = ICADialog.get_rejection(
data[channels][data['block_number'].isin([1, 2])]#.iloc[~get_outliers_mask(data[channels][data['block_number']<12], std=2)]
, channels, fs, mode='csp')
#(_rej, filt, topo, _unmix, _bandpass, _) = ICADialog.get_rejection(np.concatenate(list(x[y=='Left']) + l1ist(x[y=='Legs'])), channels, fs, mode='csp')
# filt, topography, bandpass, rejections = SelectSSDFilterWidget.select_filter_and_bandpass(np.concatenate(x), ch_names_to_2d_pos(channels), channels, sampling_freq=fs)
np.save('ica_{}.npy'.format(experiment), filt)
data['SMR'] = np.dot(data[channels], filt)
#data = data.iloc[~get_outliers_mask(data[['C3', 'SMR']], std=3, iter_numb=10)]
# temporal filter
data['SMR_band_filt'] = fft_filter(data['SMR'], fs, (11, 13))
data['SMR_env'] = np.abs(signal.hilbert(data['SMR_band_filt']))
data.to_pickle('data-{}.pkl'.format(experiment))
data['SMR'].plot()
data['SMR_band_filt'].plot()
data['SMR_env'].plot()
plt.show()
print(mock_ind)
df = pd.DataFrame(np.concatenate(data), columns=channels)
df['block_name'] = np.concatenate([[p] * len(d)
for p, d in zip(p_names, data)])
df['block_number'] = np.concatenate([[j + 1] * len(d)
for j, d in enumerate(data)])
df['alpha'] = np.dot(df[channels], spat)
df['alpha_env'] = df['alpha'] * 0
df['time'] = np.arange(len(df)) / fs
df['times'] = np.concatenate(times)
df['signal'] = np.concatenate(signal)[:, 0]
fig, axes = plt.subplots(1, 5)
eeg = fft_filter(df.loc[df['block_name'].isin(['Legs', 'Left']), channels], fs,
band)
topo = np.dot(np.dot(eeg.T, eeg), spat)
axes[2].set_xlabel('Spat. filt.')
plot_topomap(spat, montage.get_pos(), axes=axes[2], show=False, contours=0)
axes[3].set_xlabel('Topography')
plot_topomap(topo, montage.get_pos(), axes=axes[3], show=False, contours=0)
axes[0].plot(
df.loc[df['block_number'] == 1, 'time'],
1000000 * fft_filter(df.loc[df['block_number'] == 1, 'alpha'], fs,
(2, 100)))
axes[0].set_xlim(3, 4)
axes[0].set_xlabel('Time, s')
axes[0].set_ylabel('Voltage, $\mu V$')
#axes[2].plot(fft_filter(df.loc[df['block_number']==2, 'alpha'], fs, (1, 45)))
from scipy.io import loadmat
import pandas as pd
from scipy.signal import hilbert, welch
import pylab as plt
from pynfb.postprocessing.utils import fft_filter
import numpy as np
from pynfb.protocols.ssd.topomap_selector_ica import ICADialog
from PyQt4 import QtGui
df = pd.read_pickle('p4')
fs = 1000
band = (8, 14)
df['SMRenv'] = pd.Series(fft_filter(df['SMR'], fs,
band)).rolling(fs * 100,
center=True).std()
df['C3env'] = pd.Series(fft_filter(df['C3'], fs,
band)).rolling(fs * 100, center=True).std()
f, ax = plt.subplots(2)
ax[0].plot(df['C3'])
ax[0].plot(-df['SMR'])
ax[0].legend(['C3', 'SMR-Right-hand'])
ax[1].plot(df['C3env'])
ax[1].plot(df['SMRenv'])
ax[1].legend(['C3', 'SMR-Right-hand'])
#plt.plot(fft_filter(df['SMR'], fs, band), alpha=0.5)
plt.show()
def preproc(x, fs, rej=None):
x = dc_blocker(x)
x = fft_filter(x, fs, band=(3, 45))
if rej is not None:
x = np.dot(x, rej)
return x
from scipy.io import loadmat
import pandas as pd
from scipy.signal import hilbert, welch
import pylab as plt
from pynfb.postprocessing.utils import fft_filter
import numpy as np
from pynfb.protocols.ssd.topomap_selector_ica import ICADialog
df = pd.read_pickle('p4')
fs = 1000
band = (8, 14)
df['SMRenv'] = pd.Series(fft_filter(df['SMR'], fs, band)).rolling(fs*100, center=True).std()
df['C3env'] = pd.Series(fft_filter(df['C3'], fs, band)).rolling(fs*100, center=True).std()
f, ax = plt.subplots(2)
ax[0].plot(df['C3'])
ax[0].plot(-df['SMR'])
ax[0].legend(['C3', 'SMR-Right-hand'])
ax[1].plot(df['C3env'])
ax[1].plot(df['SMRenv'])
ax[1].legend(['C3', 'SMR-Right-hand'])
#plt.plot(fft_filter(df['SMR'], fs, band), alpha=0.5)
plt.show()
